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Allow passing epsilon_factor in gradient functions #52

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merged 2 commits into from
May 20, 2019

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andreasnoack
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It can be useful when the objective function is computed in lower precision e.g. when it's a numerical solution to an ODE.

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Coverage Status

Coverage increased (+15.3%) to 57.143% when pulling 4d5fac9 on andreasnoack:an/eps into 21eba38 on JuliaDiffEq:master.

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coveralls commented May 20, 2019

Coverage Status

Coverage decreased (-1.6%) to 40.201% when pulling 2416927 on andreasnoack:an/eps into 21eba38 on JuliaDiffEq:master.

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Coverage Status

Coverage increased (+15.3%) to 57.143% when pulling 4d5fac9 on andreasnoack:an/eps into 21eba38 on JuliaDiffEq:master.

@ChrisRackauckas
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I seems like this should be a uniform change, i.e. should be done to the derivative and Jacobian functions as well?

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codecov bot commented May 20, 2019

Codecov Report

Merging #52 into master will decrease coverage by 0.45%.
The diff coverage is 90%.

Impacted file tree graph

@@            Coverage Diff             @@
##           master      #52      +/-   ##
==========================================
- Coverage   58.47%   58.02%   -0.46%     
==========================================
  Files           6        6              
  Lines         301      293       -8     
==========================================
- Hits          176      170       -6     
+ Misses        125      123       -2
Impacted Files Coverage Δ
src/derivatives.jl 70.83% <100%> (+0.83%) ⬆️
src/jacobians.jl 82.75% <100%> (-0.58%) ⬇️
src/gradients.jl 51.53% <80%> (-0.57%) ⬇️

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@andreasnoack
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Makes sense. I've added the keyword argument to the remaining methods.

@ChrisRackauckas ChrisRackauckas merged commit 7e5f5cb into JuliaDiff:master May 20, 2019
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3 participants